ORIGINAL ARTICLE

Spatial and temporal dimensions of landscape fragmentationacross the Brazilian Amazon

Isabel M. D. Rosa1,4• Cristina Gabriel2 • Joao M. B. Carreiras3

Received: 14 September 2015 / Accepted: 3 February 2017

� The Author(s) 2017. This article is published with open access at Springerlink.com

Abstract The Brazilian Amazon in the past decades has

been suffering severe landscape alteration, mainly due to

anthropogenic activities, such as road building and land

clearing for agriculture. Using a high-resolution time series

of land cover maps (classified as mature forest, non-forest,

secondary forest) spanning from 1984 through 2011, and

four uncorrelated fragmentation metrics (edge density,

clumpiness index, area-weighted mean patch size and

shape index), we examined the temporal and spatial

dynamics of forest fragmentation in three study areas

across the Brazilian Amazon (Manaus, Santarem and

Machadinho d’Oeste), inside and outside conservation

units. Moreover, we compared the impacts on the land-

scape of: (1) different land uses (e.g. cattle ranching, crop

production), (2) occupation processes (spontaneous vs.

planned settlements) and (3) implementation of conserva-

tion units. By 2010/2011, municipalities located along the

Arc of Deforestation had more than 55% of the remaining

mature forest strictly confined to conservation units. Fur-

ther, the planned settlement showed a higher rate of forest

loss, a more persistent increase in deforested areas and a

higher relative incidence of deforestation inside conserva-

tion units. Distinct agricultural activities did not lead to

significantly different landscape structures; the accessibil-

ity of the municipality showed greater influence in the

degree of degradation of the landscapes. Even with a high

proportion of the landscapes covered by conservation units,

which showed a strong inhibitory effect on forest frag-

mentation, we show that dynamic agriculturally driven

economic activities, in municipalities with extensive road

development, led to more regularly shaped, heavily frag-

mented landscapes, with higher densities of forest edge.

Keywords Fragmentation � Brazilian Amazon �Deforestation � Forest regeneration � Land use �Conservation � Settlements

Introduction

Several studies have shown that land colonisation and land

use activities, which can be perpetrated by different agents,

lead to distinct spatial patterns in the landscape (e.g. Godar

et al. 2014, Wang and Caldas 2014) and patch size distri-

butions over time (Rosa et al. 2012). Understanding how

landscapes evolved through time as a result of different

land use activities, policies and anthropogenic pressures is

Editor: Sarah Gergel.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10113-017-1120-x) contains supplementarymaterial, which is available to authorized users.

& Isabel M. D. Rosa

isabel.rosa@idiv.de

Cristina Gabriel

cristinagarciagabriel@gmail.com

Joao M. B. Carreiras

j.carreiras@sheffield.ac.uk

1 Life Sciences Department, Imperial College of London,

Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK

2 Centro de Ecologia Aplicada Prof. Baeta Neves, Instituto

Superior de Agronomia, Tapada da Ajuda,

1349 - 017 Lisbon, Portugal

3 National Centre for Earth Observation (NCEO), University of

Sheffield, Hicks Building, Hounsfield Road,

Sheffield S3 7RH, UK

4 Biodiversity Conservation Group, German Centre for

Integrative Biodiversity Research (iDiv), Deutscher Pl. 5E,

04103 Leipzig, Germany

123

Reg Environ Change

DOI 10.1007/s10113-017-1120-x

essential to identify potential ecological impacts, such as

biodiversity and habitat loss (Laurance et al. 2011), and to

support efficient policy design (Wang and Caldas 2014).

Furthermore, it is critical to know how the spatial pattern of

conversion emerged over time, as both patchiness and total

amount of remaining habitat can have distinct impacts on

biodiversity (Fahrig 2013), and carbon storage can vary by

twofold to fourfold depending on the emerging pattern

(Chaplin-Kramer et al. 2015).

One of the consequences of forest disturbance is the

fragmentation of the landscape, leading to a mixture of

land cover patches of different classes, sizes and shapes

(Numata et al. 2011). Landscape fragmentation induces

major changes in the equilibrium of the ecosystems

(Haddad et al. 2015), such as fundamental alterations to the

aboveground biomass dynamics (Nascimento and Laurance

2004), marked differences in species richness and abun-

dance (Ochoa-Quintero et al. 2015) and extinction of native

biota (Pardini et al. 2010).

The Brazilian Amazon has been subjected to extensive

deforestation in the past four decades (Davidson et al.

2012). Annual deforestation mapping generated from

satellite data has been made publicly available by the

Brazilian National Institute for Space Research (Instituto

Nacional de Pesquisas Espaciais, INPE) since 1988 within

the scope of the PRODES project (INPE 2013). Reported

deforestation rates have been highly variable over time,

reaching its highest value in the mid-1990s

(*30,000 km2 yr-1), but decreased progressively since

the mid-2000s up to a record low of *4500 km2 yr-1 in

2012. These fluctuations in the deforestation rate of the

Brazilian Amazon can be attributed to the combination of

many socio-economic factors, which varied over time

(Ewers et al. 2008, Brondizio and Moran 2012). In par-

ticular, between the 1970s and 1980s, deforestation was

mainly a result of government-sponsored initiatives to

colonise the region, which featured extensive road build-

ing, granting land titles to settlers and tax incentives

(Fearnside 2005). Beginning in the 1990s, and up until

today, national and international commodities demand,

such as soybeans and beef, started playing a stronger role in

the temporal variations of annual deforestation rates

(Nepstad et al. 2006; Laurance 2007). The unsustainable

rates of deforestation observed in the late 1990s and early

2000s were successfully reduced by a combination of

stronger law enforcement (e.g. command and control

operations), expansion of the conservation units’ network

and the implementation of the best forest monitoring sys-

tem in the world (Nepstad et al. 2014). These were also

helped by an international economic crisis as well as by

fair-trade initiatives to prevent the commercialisation of

products that led to illegal deforestation in the Brazilian

Amazon, such as the soy moratorium (Gibbs et al. 2015).

Most of the deforested area is currently under agricultural

use, especially for cattle ranching; however, land aban-

donment has occurred in many areas, thus allowing the

expansion of secondary succession forest of different ages

(Davidson et al. 2012).

Landscape metrics have often been used to evaluate

forest fragmentation and land cover patterns over time in

many environments and regions (e.g. Lung and Schaab

2006; Weng 2007; Peng et al. 2010). Particularly in the

Brazilian Amazon, Wang and Caldas (2014) used multi-

temporal Landsat data and three landscape metrics to show

how different types of settlements in the state of Para—

spontaneous colonisation versus social movement organi-

sation-led settlements—impacted forest fragmentation over

time. Similarly, Batistella et al. (2003) used these metrics

to investigate land change in two adjacent settlements in

Rondonia and understand how their different designs

impacted the landscape. At a coarser scale, Colson et al.

(2011) quantified and investigated the spatial patterns of

two land uses based on eight landscape metrics, in the

states of Para, Mato Grosso, Rondonia and Amazonas.

Finally, using data that covered two decades of deforesta-

tion in Rondonia, Frohn and Hao (2006) evaluated the

performance of sixteen landscape metrics at different spa-

tial scales.

In the Brazilian Amazon, fragmentation has been

mainly a result of anthropogenic activities, such as road

building, logging, or clearing land for agriculture activi-

ties and associated occurrence of wildfires, and to a much

lesser extent the occurrence of natural events such as

blowdowns (Ahmed et al. 2013; Aragao and Shimabukuro

2010, Brando et al. 2014; Espırito-Santo et al. 2005).

Taking advantage of having quasi-annual land cover maps

spanning nearly 30 years, and classified into mature for-

est, non-forest and secondary forest classes, on different

states of the Amazon (Carreiras et al. 2014), the main

goal of this study was to examine the size, shape and

aggregation of land cover patches undergoing distinct

rates and fragmentation patterns over time. In particular,

we investigated the temporal and spatial dimensions of

landscape fragmentation across three study areas: Manaus

(Amazonas state), Santarem (Para) and Machadinho

d’Oeste (Rondonia), comparing the dynamics inside and

outside conservation units. Despite a strong human pres-

ence in all regions, these landscapes were colonised dif-

ferently, the land use activities practised in them were

distinct over time, and conservation activities were also

different. As such, we make inferences and associate the

observed fragmentation matrices with land use activities

(e.g. cattle ranching, crop production), occurrence of

large-scale wildfires, occupation processes (e.g. sponta-

neous, planned settlement), as well as the implementation

and effectiveness of conservation units.

I. M. D. Rosa et al.

123

Methods

Study areas

The Manaus site has an area of *5000 km2 (Figure S1)

and encompasses the majority of a federal conservation

unit: Biological Dynamics of Forest Fragments Project

(BDFFP) (Laurance et al. 2011); other county and state

conservation units were also included in this study area.

The construction of a highway connecting Manaus with

Boa Vista (BR-174) in the early 1970s caused the inception

of deforestation in the region and subsequent agricultural

expansion. In 1979, several forest fragments inside what is

now the BDFFP were preserved, prior to deforestation of

the surrounding forest, and used to study the impacts of

deforestation on ecosystem structure and function (e.g.

Uriarte et al. 2011), to inform future conservation pro-

grammes in the Amazon (Laurance et al. 2011).

The Santarem site, with *1100 km2 (Figure S1), is

partially within a federal conservation unit—Tapajos

National Forest—between the Tapajos River and the BR-

163 highway connecting Santarem with Cuiaba (Mato

Grosso). This unit was created in 1974 and has been used

successfully to implement novel forest management prac-

tices, such as the benefits of reduced impact logging on

social welfare and biodiversity (e.g. Bacha and Rodriguez

2007; van Gardingen et al. 2006).

The Machadinho d’Oeste site, with an area of

*1800 km2, is mainly located within the Machadinho

d’Oeste municipality (Figure S1). Its origins are a settle-

ment project, deployed by the Brazilian federal govern-

ment in 1982 to colonise the Amazon (Miranda 2013). The

original vegetation is dominated by open rainforests (Mi-

randa 2013), and according to Batistella and Moran (2005),

most of its inhabitants live from subsistence agriculture.

This site includes several state-level conservation units,

mainly extractive reserves, which were implemented in the

mid-1990s.

In terms of land cover (Table S1), mature forest was the

dominant class in all three regions at the beginning of the

time series: in 1984/1985 mature forest covered 90.7% of

the Machadinho d’Oeste landscape, 78.8% of Santarem and

83.3% of Manaus landscape (Carreiras et al. 2014). Over

time, mature forest declined significantly, and by the end of

the time series, in 2010/2011, Santarem and Machadinho

d’Oeste already had more than 50% of its area covered by

either non-forest or secondary forest (Carreiras et al. 2014).

Remote sensing data and image classification

Time series of 3-class land cover maps (mature forest, non-

forest and secondary forest) was obtained from automatic

classification of Landsat sensor data over the three selected

sites. Carreiras et al. (2014) and Prates-Clark et al. (2009)

provide detailed information about the remote sensing data

processing and methods used to generate the 3-class land

cover maps, as well as information on the post-processing

and corresponding accuracy assessment. In this section,

only a summary is presented, for more details please see

the above-mentioned publications.

In Manaus, Prates-Clark et al. (2009) and Carreiras et al.

(2014) used Landsat Multi-spectral Scanner (MSS), The-

matic Mapper (TM) and Enhanced Thematic Mapper Plus

(ETM?) data acquired in the periods 1973–2003 and

2006–2011, respectively, to analyse the land cover

dynamics in the region. In Santarem, Landsat TM data

were acquired between 1984 and 2003 (Prates-Clark et al.

2009) and in the period 2005–2010 (Carreiras et al. 2014),

and then classified to generate the time series of 3-class

land cover maps. In Machadinho d’Oeste, Landsat TM data

from the period 1984–2011 were used to create the time

series of 3-class land cover maps (Carreiras et al. 2014). To

maintain consistency among the three time series, only data

from 1984 onwards were used in this study (Table S2). At

all sites, most scenes were unaffected by substantive cloud

cover and overall gaps in the time series ranged from one

(70%) to four years (3%). Several parametric (minimum

distance, maximum likelihood) and nonparametric (random

forests) classification algorithms were used with the

objective of generating the best possible discrimination

among the three land cover classes at all three sites.

Fragmentation metrics

We used FRAGSTATS 4 (McGarigal et al. 2012) to

investigate how fragmentation emerged over time in each

of our three study areas, separately for areas inside versus

outside conservation units, assessing the impact of the

occupation process and land use dynamics. Since many

landscape metrics are redundant and statistically corre-

lated, we chose a parsimonious set of uncorrelated metrics

to use (described below) that capture landscape configu-

ration (e.g. area, shape) and aggregation.

The scale of our analyses was focused at the class level,

because we were interested in investigating changes over

time on the three existing land cover classes (mature forest,

non-forest and secondary forest classes), rather than

changes at the patch level. Determining metrics at the

class-level implies the integration of all patches of a given

class. The statistic used to combine all patches is specified

below where we detailed the chosen metrics. We used the

eight-cell neighbours to assign a given pixel to a particular

patch.

Within each site and for each land cover class, we fitted

linear regressions to test for positive/negative trends over

Spatial and temporal dimensions of landscape fragmentation across the Brazilian Amazon

123

time on the metrics’ values, taking into account temporal

autocorrelation. These analyses were performed in R (R

Core Team 2013) using the auto.arima function to deter-

mine the temporal autocorrelation structure within the data

and then the gls function to determine the temporal struc-

ture. Time was the only independent variable for each

regression that was fitted per fragmentation metric (de-

pendent variable), per landscape (inside or outside con-

servation units) and per land cover in each landscape.

Edge density (ED)

Edge effects are some of the most important drivers of

ecological change (Laurance et al. 2011). To allow a

straightforward comparison across sites with different

areas, we chose edge density (ED) rather than total edge.

ED is determined by summing the lengths (m) of all edge

segments of the patches within each land cover class. This

is then divided by the total landscape area.

Clumpiness index (CLUMPY)

This is a normalised index that indicateswhether a certain land

cover class is aggregated or dispersed across the landscape.

CLUMPY values can fluctuate between -1 (maximally dis-

aggregated) and 1 (maximally clumped). If zero, the patch

distribution is no different than random. Values under zero

suggest greater dispersion (or disaggregation),whereas values

greater than zero suggest a more clumped landscape. This

metric is important to understand spatial connectivity across

forest patches, for example, which has strong implications on

species conservation (Donald and Evans 2006).

Finally, to investigate patch size and shape distributions in

our study sites we determined the area-weighted version of

these metrics, respectively, the area-weighted mean patch

size (AREA_AM) and area-weighted mean patch shape

index (SHAPE_AM). In both cases, the area-weighted mean

of each index is given by the sum across all patches consti-

tuting each land cover class, weighted by the proportional

abundance of the patch (McGarigal et al. 2012).

Area-weighted mean patch size (AREA_AM)

This index gives the area-weightedmean patch size of all the

patches within each land cover class, where the proportional

area of each patch is based on total land cover class area, i.e.

the total area of mature forest, secondary forest and non-

forest. We have considered this metric so we could measure

the degree of fragmentation of each land cover class, i.e.

whether there were many small patches or whether the

landscape was dominated by large patches, and how that

varied, over time, across land covers and landscapes.

Area-weighted mean patch shape index (SHAPE_AM)

This metric is given by the sum of the patch shape index

across all patches of each land cover class, multiplied by

the proportional abundance of the patch. If the value is 1,

the patch is essentially a square, and values increasingly

higher than one result in patches increasingly irregular. We

included this metric to evaluate how different anthro-

pogenic activities and/or natural events could lead to more

regularly or irregularly shaped landscapes, and again how

that varied, over time, across land covers, inside and out-

side conservation units.

Relative incidence of deforestation

inside and outside conservation units

Conservation units (CUs) play a very important role in

preventing further deforestation in the Brazilian Amazon

(Soares-Filho et al. 2010), but with the observed rise in

anthropogenic pressures over the last decades, they have

become more isolated, and their role increasingly

threatened (DeFries et al. 2005). We tested whether

these observations were also valid in our study areas, by

determining the relative incidence of deforestation (RID)

across time, both inside and outside existing CUs, given

by:

RID ¼Di

Dtotal

� �

Ai

Atotal

� � ð1Þ

where Di is the deforested area of class i (CU or non-CU),

Dtotal is the total deforested area, Ai is the area of class i,

and Atotal is the total area. Values under 1 mean that

deforestation in a given class is less than its share in the

landscape, whereas values above 1 mean that deforestation

in that class is higher than its share in the landscape. RID

was used to normalise the incidence of deforestation inside

and outside CUs by their respective areas, which is relevant

since CU and non-CU areas are different at each site and

also among sites. Then, we tested for its significance over

time similarly to what we did for the fragmentation met-

rics. Fluctuations in RID over time inform us on whether

these units in the three regions under analysis have, in fact,

been suffering higher anthropogenic pressure in recent

years.

Results

Santarem and Manaus started the time series with a (pro-

portionally) larger area of secondary and non-forest com-

pared to Machadinho d’Oeste (21.2% and 16.7% vs. 9.3%),

I. M. D. Rosa et al.

123

which was almost intact before the 1980s (90.7% mature

forest) (Table S1). Nonetheless, in all three landscapes we

observed significant changes in land cover between the first

and last time step of the time series analysed (Fig. 1). By

the end of the time series, mature forest in Machadinho

d’Oeste occupied only 31.6% of the landscape, 55.7% of

which within conservation units (more than doubling the

initial proportion of 23.4%). Santarem ended the time

series with about 46.2% of mature forest, 62.2% of which

inside CUs, whereas Manaus, by contrast, reached 2011

with 71.9% of mature forest evenly distributed inside and

outside CUs.

Fragmentation metrics

Edge density (ED)

Edge density in mature forest increased significantly over

time in all landscapes, both inside and outside CUs (Fig. 2,

Table S3). ED was higher outside CUs than inside in both

Machadinho d’Oeste and Santarem, and the steepest

increase was observed in Santarem. Here, ED varied from a

minimum of 4.8 m/ha in 1984 (inside CUs) to a maximum

of 36.0 m/ha in 1998 (outside CUs), whereas in Machad-

inho d’Oeste it varied between 0.05 m/ha (inside CUs) in

1984 and 22.7 m/ha in 2003 (outside CUs), and in Manaus

from 1.9 m/ha (inside CUs) in 1985 to 10.9 m/ha (outside

CUs) in 2011 (Fig. 2). The pattern is inverted in Manaus,

where ED increased more inside than outside conservation

units.

ED in the non-forest class has only significantly

increased over time in Machadinho d’Oeste (Table S3),

both inside and outside CUs, and in Manaus, only inside

CUs (since it decreased significantly outside). In Machad-

inho d’Oeste, ED varied from a minimum of 0.05 m/ha

inside CUs in 1984 to a maximum of 27.0 m/ha in 2007

(Fig. 2), outside CUs. Figure 2 shows two peaks in ED

values for Santarem in 1993 and 1998 that match the

occurrence of two large wildfires.

ED in secondary forest class has also increased signifi-

cantly over time outside CUs in Machadinho d’Oeste and

Santarem (Table S3). In Machadinho d’Oeste, this metric

varied from a minimum of 0.01 m/ha (inside CUs) in 1986

to a maximum of 41.8 m/ha in 2011 (outside CUs),

whereas in Santarem it varied between 7.8 m/ha in 1987

and 64.4 m/ha (the peak observed for 1999, outside CUs, in

Fig. 2).

In all three regions, and for all three land cover classes,

this metric’s value was always lower inside CUs than

outside throughout the time series, and it increased over

time (except for NF in Manaus). Machadinho d’Oeste

showed the highest magnitudes of changes in ED for all

three classes, both inside and outside CUs.

Clumpiness index (CLUMPY)

Clumpiness Index (CLUMPY) values were always greater

than 0.2 throughout the time period, in all three regions,

Fig. 1 Land cover (mature forest—green, non-forest—red and

secondary forest—orange) at the beginning and by the end of our

time period of analysis in Machadinho d’Oeste: a 1984 and b 2011,

Santarem: c 1984 and d 2010, and Manaus: e 1985 and f 2011. Blacksolid lines show the boundaries of the conservation units; blue dashed

lines show the road network in 2010

Spatial and temporal dimensions of landscape fragmentation across the Brazilian Amazon

123

and for all three land cover classes (Fig. 3), inside and

outside CUs. Both inside and outside CUs, CLUMPY in

mature forest decreased significantly over time in

Machadinho d’Oeste, from 0.97 in 1984 to 0.89 in 2011

(outside CUs) and from 0.99 in 1984 to 0.98 in 2011 (in-

side CUs); in Manaus, from 0.97 in 1985 to 0.96 in 2011,

and from 0.99 in 1985 to 0.97 in 2011 (inside CUs); and in

Santarem, from 0.97 in 1984 to 0.85 in 2010, and from 0.99

in 1985 to 0.95 in 2010 (inside CUs).

In Machadinho d’Oeste and Santarem, both inside and

outside CUs, our results showed a significant increase in

CLUMPY in non-forest (Table S3), contrarily to Manaus

where the relationship was non-significant. Strong oscilla-

tions for this parameter were observed in Santarem

(Fig. 3). The CLUMPY values in Machadinho d’Oeste

varied between 0.59 in 1984 and 0.91 in 2011 (outside

CUs) and from 0.43 to 0.92 inside CUs, whereas in San-

tarem these varied between 0.69 in 1984 and 0.91 in 2010,

outside CUs, and from 0.65 to 0.89 inside CUs, for the

same years (Fig. 3).

In Machadinho d’Oeste and Manaus, both inside and

outside CUs, we observed an increase in the values of

CLUMPY in secondary forest (Table S3), which is

consistent with the values obtained for non-forest,

which are necessarily linked because the occurrence of

secondary forest needs to be preceded by non-forest.

This metric varied from 0.38 in 1986 to 0.82 in 2011,

outside CUs, and from 0.24 to 0.84 inside CUs in

Machadinho d’Oeste; in Manaus from 0.72 in 1985 to

0.87 in 2011 outside CUs and from 0.76 to 0.83 inside

CUs.

For the majority of the time steps (69%) in the time

series analysed, CLUMPY values were higher inside CUs

than outside, for all three land cover classes and in all three

regions. This was particularly true for secondary forest

(78%) and mature forest (75%).

Fig. 2 Edge density (ED)

calculated for each land cover

class [mature forest (MF), non-

forest (NF), and secondary

forest (SF)] in Machadinho

d’Oeste, Santarem and Manaus

between 1984 and 2011, a–c inside and d–f outsideconservation units

I. M. D. Rosa et al.

123

Area-weighted mean patch size (AREA_AM)

At the beginning of the time period covered in this study,

the largest patches belonged to mature forest in the three

landscapes (Fig. 4). However, by the end of the time period

that was still true only for Manaus. In all three regions, a

significant and sharp reduction in the AREA_AM values

was observed for this land cover class (Table S3), both

inside and outside CUs. The steepest reductions were

observed outside CUs: in Manaus, with an AREA_AM of

246,122 ha in 1985 and 46,415 ha by 2011; followed by

Machadinho d’Oeste, with the values of AREA_AM

declining from 77,375 ha in 1984 to 211.7 ha in 2011; and

Santarem, where AREA_AM varied from 48,672 ha in

1984 to 5446 ha in 2010 (Fig. 4).

Conversely, we found a significant positive trend in the

AREA_AM of secondary forest in all three regions, both

inside and outside CUs, although higher outside. This

increase was particularly sharper in Santarem, where the

AREA_AM index varied from 231.8 ha in 1984 to

25,956 ha in 2010; followed by Manaus and Machadinho

d’Oeste, where AREA_AM varied between approximately

168.4 and 1.4 ha in 1985/1986 to 3347 and 633.1 ha in

2011, respectively. Regarding non-forest, only in

Machadinho d’Oeste, both outside and inside CUs, we

found a significant increase in the AREA_AM values over

time (Table S3).

Area-weighted mean patch shape index (SHAPE_AM)

The results obtained for this metric showed a less clear

trend for all three regions (Fig. 5). For mature forest, out-

side CUs, there was a significant decrease in the values of

SHAPE_AM both in Machadinho d’Oeste and Manaus. In

Manaus, this metric changed from a maximum of 24.6 in

1985 and a minimum of 9.6 in 2011, whereas in

Fig. 3 Clumpiness index

(CLUMPY) determined for

each land cover class [mature

forest (MF), non-forest (NF),

and secondary forest (SF)] in

Machadinho d’Oeste, Santarem

and Manaus between 1984 and

2011, a–c inside and d–f outsideconservation units

Spatial and temporal dimensions of landscape fragmentation across the Brazilian Amazon

123

Machadinho d’Oeste it oscillated between 7.6 in 1984,

reaching a maximum of 13.2 in 1990, and a minimum

value of 3.0 by the end of the time period. The opposite

trend was observed in Santarem (Table S3), where this

metric varied between a minimum of 10.0 in 1991 and a

maximum of 15.1 in 2001, ending the time period at a

value of 14.2.

With regard to non-forest and secondary forest, in

Machadinho d’Oeste we found significant trends

(Table S3), with both becoming increasingly irregular over

time inside and outside CUs. On the other hand, in San-

tarem, only secondary forest was significantly more irreg-

ular over time, both inside and outside CUs. In both

regions, irregularity was higher outside than inside CUs.

No significant trend was observed neither in Manaus (non-

forest and secondary forest) nor in non-forest in Santarem,

due to the strong fluctuations in the value of this metric

(Fig. 5).

Relative incidence of deforestation inside/outside

conservation units

By the end of the time period analysed, in Manaus, the area

of mature forest comprised 78.0% of the conservation units

(CUs) and 67.2% of the outside, whereas in Santarem and

Machadinho d’Oeste it encompasses 78.0% and 75.7% of

the CUs, respectively, and only 27.6% and 18.2% of the

outside (Table S1, Fig. 1). In Santarem, we found an

overall significant increase in relative incidence of defor-

estation (RID) outside CUs (Table S4 and Figure S2),

whereas inside it decreased over time. In Manaus, we

found that RID increased in the State CU, but decreased in

the County and Federal CUs as well as in the areas under

no protection (Table S4). Areas that do not have any

conservation status in Santarem and Machadinho d’Oeste

had much higher RID values than those who have, with the

year of implementation not having any impact on this

Fig. 4 Area-weighted mean

patch size area (AREA_AM) for

each land cover class [mature

forest (MF), non-forest (NF),

and secondary forest (SF)] in

Machadinho d’Oeste, Santarem

and Manaus between 1984 and

2011, a–c inside and d–f outsideconservation units

I. M. D. Rosa et al.

123

metric. In Machadinho d’Oeste, between the first and last

date of the time series, we found that RID inside the State

CUs increased by tenfold, whereas in non-CUs it decreased

by 5%. Distinct conservation status (federal, very limited

activities vs. state, some extraction activities allowed)

might help explaining the differences found in the slopes of

the fitted regressions (Table S4).

In Manaus, the county CU had the highest RID for the

majority of the time period, with a slight increase of 0.7%

RID between the beginning and end of the time series

(Figure S2). In the federal units, there was a *50%

reduction in RID; however, the high RID observed in the

federal CU of Manaus can be explained by the fact that this

unit contains the BDFFP research sites (Laurance et al.

2011). Finally, we also determined a 70% increase in RID

inside State CUs of Manaus, and a 12% reduction in the

RID outside CUs.

Discussion

Emerging fragmentation patterns

Although evidence of landscape fragmentation was

observed in all landscapes, we found strong differences

across regions, as well as over time on the three land cover

types within regions. Our findings show that Manaus,

which is located in the north-western part of the Amazon,

far from the dynamic Arc of Deforestation, was the least

fragmented landscape by the end of the time period

(Fig. 1). Nonetheless, an increasing edge density (ED) and

a strong decrease in the patch area (AREA_AM) indices in

the mature forest class (Figs. 2 and 4) indicate that the old-

growth forest is indeed being fragmented.

In Santarem and Machadinho d’Oeste, both located in

areas where agro-businesses are well established (Barona

Fig. 5 Area-weighted mean

patch shape index

(SHAPE_AM) for each land

cover class [mature forest (MF),

non-forest (NF), and secondary

forest (SF)] in Machadinho

d’Oeste, Santarem and Manaus

between 1984 and 2011, a–c inside and d–f outsideconservation units

Spatial and temporal dimensions of landscape fragmentation across the Brazilian Amazon

123

et al. 2010), and road networks are extensive (Ahmed et al.

2013), mature forest now represents less than 50% the

landscape and is almost strictly confined to conservation

units (Fig. 1). Further, ED has risen significantly faster in

these landscapes compared to Manaus, which is a result of

higher deforestation rates. In Machadinho d’Oeste, the

value of this metric rose steadily over time, whereas in

Santarem we observed several peaks (Fig. 2), most likely

to be related to the occurrence of wildfires, associated with

agriculture activities, which caused a more scattered pat-

tern of fragmentation (replicated for the CLUMPY and

SHAPE_AM metrics). Fire is often used as a tool in agri-

culture; however, farmers sometimes lose control of it,

leading to uncontrolled wildfires (Brando et al. 2014). The

occurrence of an El Nino Southern Oscillation event in

1997–1998 promoted extremely dry weather conditions,

which helped the propagation of the late 1990s large

wildfires. These wildfires tend to leave a more irregularly

shaped scar on the landscape than do the anthropogenic

activities that lead to deforestation, such as converting

forest to pastures and/or croplands.

With regard to patch size distributions (AREA_AM), by

the end of the time period, on the contrary with what was

observed for Manaus, the secondary forest in Santarem,

and the non-forest class in Machadinho d’Oeste, had the

largest patches. Such result in Machadinho d’Oeste can be

due to either (1) a change in the agents of deforestation or

their motivations in the region that now deforest larger

areas; or (2) further support to previous findings that

deforestation tends to occur next to already deforested

areas, leading to larger patches of non-forest over time

through coalescence of spatially contiguous patches of

deforested land (Rosa et al. 2013, 2015). Regarding San-

tarem, wildfires were major contributors to the large

amount of secondary forest that was generated following

these events in the 1990s. However, it is possible that the

consequence of these events was not a full conversion from

mature to secondary forest, but rather to a mosaic of mature

and secondary forest species. From a pure remote sensing

point of view, it was difficult to ascertain the degree of

damage caused by these wildfires, and therefore, all area

was considered to have first transitioned to non-forest

(burnt scar) and subsequently to secondary forest (Carreiras

et al. 2014).

While the overall pattern of the landscape metrics was

similar inside and outside conservation units, showing a

forest getting increasingly scattered and larger non-forest

patches emerging over time, we still found important dif-

ferences. In all three regions, fragmentation was more

severe outside conservation units with higher increases in

ED and steeper reductions in the area of mature forest.

Although this suggests that CUs have had a role in pre-

venting further fragmentation, in agreement with previous

findings (Barber et al. 2014), our RID analysis in

Machadinho d’Oeste, the landscape that has undergone the

highest land cover change, has shown that existing CUs are

suffering increasing pressure from deforestation activities

due to significant reduction in unprotected forested areas.

Land use dynamics and occupation processes

In the time series analysed, both Santarem and Manaus

start off with a higher proportion of secondary forest and

non-forest compared to Machadinho d’Oeste, which

reflects the fact that these two regions were spontaneously

colonised in the 1970s, whereas Machadinho d’Oeste was

mainly intact before the 1980s (Table S1). The fact that

Manaus was the least fragmented landscape can be

explained in part by: (1) its isolation—road density in this

municipality is much lower when compared to the

municipalities in the Arc of Deforestation (Ahmed et al.

2013); and (2) by a lack of favourable conditions for

agriculture—wetter weather and poorer soils (Fearnside

2005). However, the increasing trend in mature forest

fragmentation in this region is most likely a result of a rise

in agriculture activities. According to the Brazilian Insti-

tute of Geography and Statistics (IBGE, http://www.sidra.

ibge.gov.br/), especially permanent cultures of coconuts

and oranges are increasing in the region (Figure S3). In

Manaus, cattle ranching still constitutes a fairly low share

of just 0.16% of the municipality’s gross domestic product

(Giatti et al. 2014).

Santarem and Machadinho d’Oeste sites are very dif-

ferent from each other, and from the Manaus site, in terms

of history of occupation and land use activities. Machad-

inho d’Oeste was a planned settlement part of a colonisa-

tion program implemented by the government in the 1980s

(Batistella and Moran 2005). Past studies have shown that

creating a new settlement has an immense impact on forest

fragmentation (Wang and Caldas 2014), and Machadinho

d’Oeste was no different. The people who live there

nowadays depend heavily on agricultural activities for

income, especially cattle ranching and coffee plantations

(Gomes et al. 2009; Miranda et al. 2008). As a result, the

number of cattle heads in Machadinho d’Oeste, according

to IBGE, rose rapidly from nearly zero in the 1980s to

more than 250,000 by 2010, representing a density of 0.31

heads/ha (Figure S3). Further, by 2014 the area dedicated

to pastures occupied 40% of the landscape (Ferreira et al.

2014). Such increase came at the cost of forest to establish

pastures, a pattern supported by our analyses, which show

higher ED, smaller and more irregular mature forest frag-

ments. Additionally, the area harvested of permanent crops

has also increased over time (Figure S3), but seems to have

been decreasing slightly over the last few years. This could

be due to an increased investment on cattle ranching or the

I. M. D. Rosa et al.

123

lack of forested lands to move into, since most of the

remaining mature forest is located within CUs (Fig. 1),

which are illegal to deforest.

The history of land use activities in Santarem, on the

other hand, is closely related to the BR-163 road, which

was built by the government in the 1970s, as part of the

Amazon colonisation program. This allowed farmers to

spontaneously establish themselves along this road. Ini-

tially, farmers in this region were mainly small-scale

agrarian reform colonists. However, and despite being

illegal, many of these farmers were pressured to sell their

land to large-scale agribusinesses (Barros 2010). Land

availability, the perspective of paving of the BR-163 road,

thus having an easy way to transport their goods, and the

existence of a port in the city of Santarem, turned this

region into a very attractive location for agriculture activ-

ities, and associated unofficial road expansion (Viana and

Fonseca 2009; Fearnside 2007). The area harvested of

annual crops has risen rapidly since the early 2000s (Fig-

ure S3). This pattern is attributed to the fast expansion of

soybean production in this municipality, supported by the

improvements made on the Santarem port by Cargill

(Barros 2010). Such dynamics has significantly impacted

the landscape, as our results show, with mature forest

becoming fragmented into smaller and more irregular

patches, thus resulting in larger edge densities.

In comparison with Santarem, deforestation was more

persistent over time in Machadinho d’Oeste, because

farmers took advantage of having more fertile soils and

higher water availability (Brondizio and Moran 2012).

Thus, their focus was on permanent crops (coffee, cocoa

and sugar cane) and low cattle ranching investment to

maintain their farm profitability (Brondizio and Moran

2012). The last remaining large patches of mature forest

are concentrated in the Tapajos National Forest (Fig. 1),

but even in this National Forest it was observed an increase

in roads, which suggests that colonists are moving inside

this CU (Viana and Fonseca 2009), also supported by our

findings of an increasing RID inside the Tapajos reserve.

We were expecting to find stronger differences in the

landscape metrics for Santarem and Machadinho d’Oeste.

In terms of rate of change, Machadinho d’Oeste was the

region where the forest was fragmented at a faster pace,

which is in line with the findings of Wang and Caldas

(2014), who argued that spontaneous settlements, such as

the Santarem site, change at a slower rate when compared

to planned settlements (the Machadinho d’Oeste site). In

terms of spatial configuration of the landscapes, we

expected pastures and permanent agriculture to lead to

larger, more aggregated, and more regularly shaped land-

scapes due to the lower need of finding new land for new

plantations, as opposed to temporary agriculture which is

much more dynamic and would occur on smaller,

dispersed, and more irregularly shaped patches. However,

overall, the patterns observed in the landscape metrics for

Santarem and Machadinho d’Oeste were similar. The

greatest differences were attributed to the fire scars left

behind by the occurrence of wildfires, associated with

agriculture activities, rather than any particular land use

policy or change in agricultural production. This is a result

supported at a coarser scale by Colson et al. (2011). The

authors found that different land use policies led to larger

discrepancies in landscape patterns at an early stage;

however, agricultural activities and cattle ranching were

later responsible for larger similarities across landscapes.

The importance of understanding fragmentation

In this study, we used a long time series of quasi-annual

data of mature forest, non-forest and secondary forest in

three municipalities of the Amazon that have been under-

going distinct land cover change and land use activities.

Our time series started in the early 1980s and ended in the

early 2010s, thus covering all major fluctuations observed

in the land cover change dynamics of the Brazilian Ama-

zon (INPE 2013). Understanding fragmentation patterns in

the Brazilian Amazon, which is so incredibly diverse, not

only in ecological terms, but also in human-related activ-

ities, is vital for designing policies that can effectively

manage deforestation in the region (Wang and Caldas

2014) and prevent further ecological impacts (Laurance

et al. 2011). Policies, such as expansion of CUs network,

and market-based initiatives have been successful in

reducing deforestation rates in the Brazilian Amazon

(Nepstad et al. 2014; Godar et al. 2014). However, it seems

that these were only efficient in reducing large-scale

deforestation (Rosa et al. 2012), more susceptible to fines

and embargos by enforcement agencies (Godar et al. 2014).

Further, now that the remaining mature forest, at least in

the municipalities along the Arc of Deforestation, is

becoming strictly contained within CUs, these will likely

suffer stronger pressures from anthropogenic activities, as

our results in Machadinho d’Oeste suggest. The differences

found between our study sites show that conservation

policies designed to prevent further deforestation in the

Brazilian Amazon need to account for temporal dynamics

and location-specific processes and agents of land cover

change, to ensure their success. Having detailed spatially

explicit information to assess land use and land manage-

ment impacts is the first critical step to understand histor-

ical changes and to be able to improve conservation

policies.

Acknowledgements This study was supported by the

‘‘REGROWTH-BR—Remote sensing of regenerating tropical forests

in Brazil: mapping and retrieving biophysical parameters’’ project

Spatial and temporal dimensions of landscape fragmentation across the Brazilian Amazon

123

(Ref. PTDC/AGR-CFL/114908/2009), funded by the Foundation for

Science and Technology (FCT, Portugal). J.M.B. Carreiras was par-

tially funded by NERC National Centre for Earth Observation (Ref:

R8/H12/82). I.M.D. Rosa was supported by the European Research

Council (Project Number 281986) and is now supported by the

European Union’s Horizon 2020 research and innovation programme

under the Marie Sklodowska-Curie Grant Agreement No 703862.

Open Access This article is distributed under the terms of the

Creative Commons Attribution 4.0 International License (http://crea

tivecommons.org/licenses/by/4.0/), which permits unrestricted use,

distribution, and reproduction in any medium, provided you give

appropriate credit to the original author(s) and the source, provide a

link to the Creative Commons license, and indicate if changes were

made.

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